Emphysema quantification on simulated X-rays through deep learning techniques

Emphysema quantification techniques rely on the use of CT scans, but they are rarely used in the diagnosis and management of patients with COPD; X-ray films are the preferred method to do this. However, this diagnosis method is very controversial, as there are not established guidelines to define the disease, sensitivity is low, and quantification cannot be done. We developed a quantification method based on a CNN, capable of predicting the emphysema percentage of a patient based on an X-ray image. We used real CT scans to simulate X-ray films and to calculate emphysema percentage using the LAA%. The model developed was able to calculate emphysema percentage with an LAA% mean error of 3.96, and it obtained an AUC accuracy of 90.73% for an emphysema definition of >10%, with a mean sensitivity of 85.68%, significantly improving X-ray-based emphysema diagnosis.